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Smart Water Security with AI and Blockchain-Enhanced Digital Twins

Mohammadhossein Homaei, Victor Gonzalez Morales, Oscar Mogollon Gutierrez, Ruben Molano Gomez, Andres Caro

TL;DR

The paper addresses secure, real-time management of rural water distribution plagued by limited monitoring and cyber risks. It proposes an integrated platform that fuses LoRaWAN data acquisition, a hybrid AI intrusion detection system (LSTM Autoencoder plus Isolation Forest), and a blockchain-enabled Digital Twin on a private Ethereum PoA network. Key findings show the system achieves over 80 transactions per second with latency under 2 seconds, supports up to 1,000 meters, and remains resilient to intermittent connectivity while ensuring tamper-resistance and transparent logging. This approach offers a practical pathway to decentralized, secure water infrastructure in under-connected rural environments and suggests directions like federated learning and dynamic pricing for broader deployment.

Abstract

Water distribution systems in rural areas face serious challenges such as a lack of real-time monitoring, vulnerability to cyberattacks, and unreliable data handling. This paper presents an integrated framework that combines LoRaWAN-based data acquisition, a machine learning-driven Intrusion Detection System (IDS), and a blockchain-enabled Digital Twin (BC-DT) platform for secure and transparent water management. The IDS filters anomalous or spoofed data using a Long Short-Term Memory (LSTM) Autoencoder and Isolation Forest before validated data is logged via smart contracts on a private Ethereum blockchain using Proof of Authority (PoA) consensus. The verified data feeds into a real-time DT model supporting leak detection, consumption forecasting, and predictive maintenance. Experimental results demonstrate that the system achieves over 80 transactions per second (TPS) with under 2 seconds of latency while remaining cost-effective and scalable for up to 1,000 smart meters. This work demonstrates a practical and secure architecture for decentralized water infrastructure in under-connected rural environments.

Smart Water Security with AI and Blockchain-Enhanced Digital Twins

TL;DR

The paper addresses secure, real-time management of rural water distribution plagued by limited monitoring and cyber risks. It proposes an integrated platform that fuses LoRaWAN data acquisition, a hybrid AI intrusion detection system (LSTM Autoencoder plus Isolation Forest), and a blockchain-enabled Digital Twin on a private Ethereum PoA network. Key findings show the system achieves over 80 transactions per second with latency under 2 seconds, supports up to 1,000 meters, and remains resilient to intermittent connectivity while ensuring tamper-resistance and transparent logging. This approach offers a practical pathway to decentralized, secure water infrastructure in under-connected rural environments and suggests directions like federated learning and dynamic pricing for broader deployment.

Abstract

Water distribution systems in rural areas face serious challenges such as a lack of real-time monitoring, vulnerability to cyberattacks, and unreliable data handling. This paper presents an integrated framework that combines LoRaWAN-based data acquisition, a machine learning-driven Intrusion Detection System (IDS), and a blockchain-enabled Digital Twin (BC-DT) platform for secure and transparent water management. The IDS filters anomalous or spoofed data using a Long Short-Term Memory (LSTM) Autoencoder and Isolation Forest before validated data is logged via smart contracts on a private Ethereum blockchain using Proof of Authority (PoA) consensus. The verified data feeds into a real-time DT model supporting leak detection, consumption forecasting, and predictive maintenance. Experimental results demonstrate that the system achieves over 80 transactions per second (TPS) with under 2 seconds of latency while remaining cost-effective and scalable for up to 1,000 smart meters. This work demonstrates a practical and secure architecture for decentralized water infrastructure in under-connected rural environments.
Paper Structure (24 sections, 4 equations, 9 figures, 2 tables, 3 algorithms)

This paper contains 24 sections, 4 equations, 9 figures, 2 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. DT in the Water Industry
  4. Security in Water Distribution Systems
  5. BC Integration in DT
  6. AI and ML in IDS for Critical Infrastructure
  7. Proposed Platform
  8. Leakage detection
  9. AI-Based IDS for DT
  10. Design and Threat Model
  11. Feature Engineering
  12. LSTM Autoencoder Architecture
  13. Isolation Forest Outlier Detection
  14. Real-Time Detection Algorithm
  15. Integration with Blockchain
  16. ...and 9 more sections

Figures (9)

  • Figure 1: A Digital Twin Platform in the Water Industry Homaei2025
  • Figure 2: Proposed Smart contract for DT platform
  • Figure 3: Technologies in the Platform on the BC side
  • Figure 4: Night consumption Hitmap for a water meter with leakage
  • Figure 5: Comparison Normal and Median usage
  • ...and 4 more figures